Overview

Dataset statistics

Number of variables43
Number of observations2535791
Missing cells62676385
Missing cells (%)57.5%
Duplicate rows238112
Duplicate rows (%)9.4%
Total size in memory831.9 MiB
Average record size in memory344.0 B

Variable types

CAT18
UNSUPPORTED13
NUM12

Warnings

Dataset has 238112 (9.4%) duplicate rows Duplicates
Date mutation has a high cardinality: 361 distinct values High cardinality
Valeur fonciere has a high cardinality: 108435 distinct values High cardinality
Type de voie has a high cardinality: 137 distinct values High cardinality
Code voie has a high cardinality: 16233 distinct values High cardinality
Voie has a high cardinality: 357364 distinct values High cardinality
Commune has a high cardinality: 29380 distinct values High cardinality
Section has a high cardinality: 583 distinct values High cardinality
Surface Carrez du 1er lot has a high cardinality: 17117 distinct values High cardinality
Surface Carrez du 2eme lot has a high cardinality: 11544 distinct values High cardinality
Surface Carrez du 3eme lot has a high cardinality: 3961 distinct values High cardinality
Surface Carrez du 4eme lot has a high cardinality: 1174 distinct values High cardinality
Surface Carrez du 5eme lot has a high cardinality: 473 distinct values High cardinality
Nature culture speciale has a high cardinality: 118 distinct values High cardinality
5eme lot is highly correlated with 4eme lotHigh correlation
4eme lot is highly correlated with 5eme lotHigh correlation
Type local is highly correlated with Code type localHigh correlation
Code type local is highly correlated with Type localHigh correlation
Code service CH has 2535791 (100.0%) missing values Missing
Reference document has 2535791 (100.0%) missing values Missing
1 Articles CGI has 2535791 (100.0%) missing values Missing
2 Articles CGI has 2535791 (100.0%) missing values Missing
3 Articles CGI has 2535791 (100.0%) missing values Missing
4 Articles CGI has 2535791 (100.0%) missing values Missing
5 Articles CGI has 2535791 (100.0%) missing values Missing
Valeur fonciere has 29261 (1.2%) missing values Missing
No voie has 1025638 (40.4%) missing values Missing
B/T/Q has 2426362 (95.7%) missing values Missing
Type de voie has 1053720 (41.6%) missing values Missing
Code voie has 28189 (1.1%) missing values Missing
Voie has 28354 (1.1%) missing values Missing
Code postal has 28323 (1.1%) missing values Missing
Prefixe de section has 2408956 (95.0%) missing values Missing
No Volume has 2529165 (99.7%) missing values Missing
1er lot has 1753172 (69.1%) missing values Missing
Surface Carrez du 1er lot has 2314726 (91.3%) missing values Missing
2eme lot has 2372713 (93.6%) missing values Missing
Surface Carrez du 2eme lot has 2481517 (97.9%) missing values Missing
3eme lot has 2509709 (99.0%) missing values Missing
Surface Carrez du 3eme lot has 2530577 (99.8%) missing values Missing
4eme lot has 2526555 (99.6%) missing values Missing
Surface Carrez du 4eme lot has 2534376 (99.9%) missing values Missing
5eme lot has 2531364 (99.8%) missing values Missing
Surface Carrez du 5eme lot has 2535182 (> 99.9%) missing values Missing
Code type local has 1182459 (46.6%) missing values Missing
Type local has 1182459 (46.6%) missing values Missing
Identifiant local has 2535791 (100.0%) missing values Missing
Surface reelle bati has 1184176 (46.7%) missing values Missing
Nombre pieces principales has 1184176 (46.7%) missing values Missing
Nature culture has 792958 (31.3%) missing values Missing
Nature culture speciale has 2422938 (95.5%) missing values Missing
Surface terrain has 792958 (31.3%) missing values Missing
No disposition is highly skewed (γ1 = 70.78417421) Skewed
Nombre de lots is highly skewed (γ1 = 28.12730824) Skewed
Surface reelle bati is highly skewed (γ1 = 188.7084892) Skewed
Surface terrain is highly skewed (γ1 = 44.79390898) Skewed
Code service CH is an unsupported type, check if it needs cleaning or further analysis Unsupported
Reference document is an unsupported type, check if it needs cleaning or further analysis Unsupported
1 Articles CGI is an unsupported type, check if it needs cleaning or further analysis Unsupported
2 Articles CGI is an unsupported type, check if it needs cleaning or further analysis Unsupported
3 Articles CGI is an unsupported type, check if it needs cleaning or further analysis Unsupported
4 Articles CGI is an unsupported type, check if it needs cleaning or further analysis Unsupported
5 Articles CGI is an unsupported type, check if it needs cleaning or further analysis Unsupported
Code departement is an unsupported type, check if it needs cleaning or further analysis Unsupported
No Volume is an unsupported type, check if it needs cleaning or further analysis Unsupported
1er lot is an unsupported type, check if it needs cleaning or further analysis Unsupported
2eme lot is an unsupported type, check if it needs cleaning or further analysis Unsupported
3eme lot is an unsupported type, check if it needs cleaning or further analysis Unsupported
Identifiant local is an unsupported type, check if it needs cleaning or further analysis Unsupported
Nombre de lots has 1753172 (69.1%) zeros Zeros
Surface reelle bati has 316200 (12.5%) zeros Zeros
Nombre pieces principales has 406960 (16.0%) zeros Zeros

Reproduction

Analysis started2020-10-06 07:25:57.544269
Analysis finished2020-10-06 07:35:23.537574
Duration9 minutes and 25.99 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Code service CH
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2535791
Missing (%)100.0%
Memory size19.3 MiB

Reference document
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2535791
Missing (%)100.0%
Memory size19.3 MiB

1 Articles CGI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2535791
Missing (%)100.0%
Memory size19.3 MiB

2 Articles CGI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2535791
Missing (%)100.0%
Memory size19.3 MiB

3 Articles CGI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2535791
Missing (%)100.0%
Memory size19.3 MiB

4 Articles CGI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2535791
Missing (%)100.0%
Memory size19.3 MiB

5 Articles CGI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2535791
Missing (%)100.0%
Memory size19.3 MiB

No disposition
Real number (ℝ≥0)

SKEWED

Distinct620
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.199535372
Minimum1
Maximum694
Zeros0
Zeros (%)0.0%
Memory size19.3 MiB
2020-10-06T09:35:24.870459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum694
Range693
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.401536829
Coefficient of variation (CV)6.170336451
Kurtosis5363.725904
Mean1.199535372
Median Absolute Deviation (MAD)0
Skewness70.78417421
Sum3041771
Variance54.78274742
MonotocityNot monotonic
2020-10-06T09:35:25.060906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1239018794.3%
 
21202324.7%
 
3164490.6%
 
429270.1%
 
512860.1%
 
6928< 0.1%
 
7664< 0.1%
 
10340< 0.1%
 
8202< 0.1%
 
9179< 0.1%
 
Other values (610)23970.1%
 
ValueCountFrequency (%) 
1239018794.3%
 
21202324.7%
 
3164490.6%
 
429270.1%
 
512860.1%
 
ValueCountFrequency (%) 
6941< 0.1%
 
6931< 0.1%
 
6921< 0.1%
 
6911< 0.1%
 
6901< 0.1%
 

Date mutation
Categorical

HIGH CARDINALITY

Distinct361
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
29/03/2019
 
22208
28/06/2019
 
22165
29/05/2019
 
18948
28/02/2019
 
17537
30/04/2019
 
17332
Other values (356)
2437601 
ValueCountFrequency (%) 
29/03/2019222080.9%
 
28/06/2019221650.9%
 
29/05/2019189480.7%
 
28/02/2019175370.7%
 
30/04/2019173320.7%
 
31/01/2019171260.7%
 
20/12/2019160890.6%
 
15/03/2019157160.6%
 
26/04/2019156520.6%
 
28/05/2019153600.6%
 
Other values (351)235765893.0%
 
2020-10-06T09:35:25.282497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2020-10-06T09:35:25.518987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Nature mutation
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
Vente
2308539 
Vente en l'état futur d'achèvement
 
185441
Echange
 
27127
Vente terrain à bâtir
 
7806
Adjudication
 
4819
ValueCountFrequency (%) 
Vente230853991.0%
 
Vente en l'état futur d'achèvement1854417.3%
 
Echange271271.1%
 
Vente terrain à bâtir78060.3%
 
Adjudication48190.2%
 
Expropriation20590.1%
 
2020-10-06T09:35:25.747834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-06T09:35:25.867938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:35:26.189081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length5
Mean length7.21120116
Min length5

Valeur fonciere
Categorical

HIGH CARDINALITY
MISSING

Distinct108435
Distinct (%)4.3%
Missing29261
Missing (%)1.2%
Memory size19.3 MiB
100000,00
 
21742
150000,00
 
21157
120000,00
 
19573
80000,00
 
18482
1,00
 
18101
Other values (108430)
2407475 
ValueCountFrequency (%) 
100000,00217420.9%
 
150000,00211570.8%
 
120000,00195730.8%
 
80000,00184820.7%
 
1,00181010.7%
 
130000,00176340.7%
 
110000,00174240.7%
 
200000,00172330.7%
 
50000,00171530.7%
 
140000,00171500.7%
 
Other values (108425)232088191.5%
 
(Missing)292611.2%
 
2020-10-06T09:35:27.298145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique32860 ?
Unique (%)1.3%
2020-10-06T09:35:29.835135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length9
Mean length8.513086449
Min length3

No voie
Real number (ℝ≥0)

MISSING

Distinct6577
Distinct (%)0.4%
Missing1025638
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean727.0275005
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Memory size19.3 MiB
2020-10-06T09:35:30.297857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median24
Q387
95-th percentile5633
Maximum9999
Range9998
Interquartile range (IQR)79

Descriptive statistics

Standard deviation2076.793685
Coefficient of variation (CV)2.856554509
Kurtosis8.223053939
Mean727.0275005
Median Absolute Deviation (MAD)20
Skewness3.062480081
Sum1097922761
Variance4313072.01
MonotocityNot monotonic
2020-10-06T09:35:30.651881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1679752.7%
 
2607032.4%
 
4482211.9%
 
3481411.9%
 
5431071.7%
 
6421811.7%
 
7377541.5%
 
8371571.5%
 
9342521.4%
 
10340581.3%
 
Other values (6567)105660441.7%
 
(Missing)102563840.4%
 
ValueCountFrequency (%) 
1679752.7%
 
2607032.4%
 
3481411.9%
 
4482211.9%
 
5431071.7%
 
ValueCountFrequency (%) 
9999261< 0.1%
 
999867< 0.1%
 
999711< 0.1%
 
999615< 0.1%
 
999515< 0.1%
 

B/T/Q
Categorical

MISSING

Distinct40
Distinct (%)< 0.1%
Missing2426362
Missing (%)95.7%
Memory size19.3 MiB
B
66043 
A
16849 
T
8914 
F
8236 
C
 
3187
Other values (35)
 
6200
ValueCountFrequency (%) 
B660432.6%
 
A168490.7%
 
T89140.4%
 
F82360.3%
 
C31870.1%
 
D14930.1%
 
Q985< 0.1%
 
E890< 0.1%
 
P574< 0.1%
 
G395< 0.1%
 
Other values (30)18630.1%
 
(Missing)242636295.7%
 
2020-10-06T09:35:30.860669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-06T09:35:31.072981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.913692414
Min length1

Type de voie
Categorical

HIGH CARDINALITY
MISSING

Distinct137
Distinct (%)< 0.1%
Missing1053720
Missing (%)41.6%
Memory size19.3 MiB
RUE
862542 
AV
195932 
RTE
 
72401
CHE
 
71983
BD
 
67221
Other values (132)
211992 
ValueCountFrequency (%) 
RUE86254234.0%
 
AV1959327.7%
 
RTE724012.9%
 
CHE719832.8%
 
BD672212.7%
 
ALL489661.9%
 
IMP351541.4%
 
PL287811.1%
 
RES199750.8%
 
QUAI78590.3%
 
Other values (127)712572.8%
 
(Missing)105372041.6%
 
2020-10-06T09:35:31.264496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2020-10-06T09:35:31.425020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length2.887699736
Min length1

Code voie
Categorical

HIGH CARDINALITY
MISSING

Distinct16233
Distinct (%)0.6%
Missing28189
Missing (%)1.1%
Memory size19.3 MiB
0020
 
11086
B005
 
11070
0040
 
10652
B007
 
10557
0060
 
10434
Other values (16228)
2453803 
ValueCountFrequency (%) 
0020110860.4%
 
B005110700.4%
 
0040106520.4%
 
B007105570.4%
 
0060104340.4%
 
B003104260.4%
 
B006102990.4%
 
B008102920.4%
 
B016102870.4%
 
B002102040.4%
 
Other values (16223)240229594.7%
 
(Missing)281891.1%
 
2020-10-06T09:35:31.709229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1603 ?
Unique (%)0.1%
2020-10-06T09:35:31.921108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.988883548
Min length3

Voie
Categorical

HIGH CARDINALITY
MISSING

Distinct357364
Distinct (%)14.3%
Missing28354
Missing (%)1.1%
Memory size19.3 MiB
LE VILLAGE
 
23596
LE BOURG
 
21852
JEAN JAURES
 
11542
DE LA REPUBLIQUE
 
11186
VICTOR HUGO
 
7845
Other values (357359)
2431416 
ValueCountFrequency (%) 
LE VILLAGE235960.9%
 
LE BOURG218520.9%
 
JEAN JAURES115420.5%
 
DE LA REPUBLIQUE111860.4%
 
VICTOR HUGO78450.3%
 
GRANDE RUE67210.3%
 
PASTEUR66830.3%
 
DE PARIS60310.2%
 
DE LA GARE59660.2%
 
DE LA LIBERATION57500.2%
 
Other values (357354)240026594.7%
 
(Missing)283541.1%
 
2020-10-06T09:35:35.256113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique126168 ?
Unique (%)5.0%
2020-10-06T09:35:35.552780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length12
Mean length12.46227745
Min length1

Code postal
Real number (ℝ≥0)

MISSING

Distinct5797
Distinct (%)0.2%
Missing28323
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean51976.36947
Minimum1000
Maximum97490
Zeros0
Zeros (%)0.0%
Memory size19.3 MiB
2020-10-06T09:35:35.765568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile7100
Q131230
median50200
Q377144
95-th percentile93300
Maximum97490
Range96490
Interquartile range (IQR)45914

Descriptive statistics

Standard deviation27302.60504
Coefficient of variation (CV)0.5252888056
Kurtosis-1.156558117
Mean51976.36947
Median Absolute Deviation (MAD)24100
Skewness-0.04981286052
Sum1.303290832e+11
Variance745432242.1
MonotocityNot monotonic
2020-10-06T09:35:35.957456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1800075260.3%
 
5110068440.3%
 
7501565820.3%
 
7501864110.3%
 
7501663900.3%
 
7660060560.2%
 
7501755740.2%
 
4430054160.2%
 
600053240.2%
 
4400051140.2%
 
Other values (5787)244623196.5%
 
(Missing)283231.1%
 
ValueCountFrequency (%) 
10001040< 0.1%
 
1090210< 0.1%
 
1100888< 0.1%
 
1110592< 0.1%
 
1120299< 0.1%
 
ValueCountFrequency (%) 
97490199< 0.1%
 
97480293< 0.1%
 
9747016< 0.1%
 
9746040< 0.1%
 
97450115< 0.1%
 

Commune
Categorical

HIGH CARDINALITY

Distinct29380
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
TOULOUSE
 
18045
NANTES
 
16576
NICE
 
15415
BORDEAUX
 
13463
LILLE
 
11705
Other values (29375)
2460587 
ValueCountFrequency (%) 
TOULOUSE180450.7%
 
NANTES165760.7%
 
NICE154150.6%
 
BORDEAUX134630.5%
 
LILLE117050.5%
 
NIMES88810.4%
 
LE HAVRE88600.3%
 
ANGERS80540.3%
 
SAINT-ETIENNE75930.3%
 
BOURGES75220.3%
 
Other values (29370)241967795.4%
 
2020-10-06T09:35:36.374229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique789 ?
Unique (%)< 0.1%
2020-10-06T09:35:36.582367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length9
Mean length11.41841264
Min length1

Code departement
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.3 MiB

Code commune
Real number (ℝ≥0)

Distinct908
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.9969296
Minimum1
Maximum909
Zeros0
Zeros (%)0.0%
Memory size19.3 MiB
2020-10-06T09:35:36.770770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q176
median174
Q3300
95-th percentile555
Maximum909
Range908
Interquartile range (IQR)224

Descriptive statistics

Standard deviation167.0736643
Coefficient of variation (CV)0.7994072674
Kurtosis0.5673820201
Mean208.9969296
Median Absolute Deviation (MAD)108
Skewness1.013132693
Sum529972533
Variance27913.60932
MonotocityNot monotonic
2020-10-06T09:35:37.019680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
63253601.0%
 
109233310.9%
 
88208270.8%
 
555196870.8%
 
7181920.7%
 
4178260.7%
 
33153740.6%
 
218151970.6%
 
194146940.6%
 
1142730.6%
 
Other values (898)235103092.7%
 
ValueCountFrequency (%) 
1142730.6%
 
299880.4%
 
3113130.4%
 
4178260.7%
 
5114950.5%
 
ValueCountFrequency (%) 
90910< 0.1%
 
90855< 0.1%
 
90746< 0.1%
 
90651< 0.1%
 
90535< 0.1%
 

Prefixe de section
Real number (ℝ≥0)

MISSING

Distinct710
Distinct (%)0.6%
Missing2408956
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean459.4845744
Minimum1
Maximum950
Zeros0
Zeros (%)0.0%
Memory size19.3 MiB
2020-10-06T09:35:37.213899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40
Q1166
median331
Q3829
95-th percentile880
Maximum950
Range949
Interquartile range (IQR)663

Descriptive statistics

Standard deviation326.0986731
Coefficient of variation (CV)0.709705377
Kurtosis-1.699463289
Mean459.4845744
Median Absolute Deviation (MAD)265
Skewness0.1634567861
Sum58278726
Variance106340.3446
MonotocityNot monotonic
2020-10-06T09:35:37.425945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16620050.1%
 
83016210.1%
 
83114710.1%
 
84414310.1%
 
83912890.1%
 
8281258< 0.1%
 
8421241< 0.1%
 
1771160< 0.1%
 
8431088< 0.1%
 
8411034< 0.1%
 
Other values (700)1132374.5%
 
(Missing)240895695.0%
 
ValueCountFrequency (%) 
1139< 0.1%
 
2106< 0.1%
 
342< 0.1%
 
4496< 0.1%
 
567< 0.1%
 
ValueCountFrequency (%) 
950158< 0.1%
 
911145< 0.1%
 
910247< 0.1%
 
90938< 0.1%
 
908165< 0.1%
 

Section
Categorical

HIGH CARDINALITY

Distinct583
Distinct (%)< 0.1%
Missing74
Missing (%)< 0.1%
Memory size19.3 MiB
A
 
182703
B
 
174753
C
 
121195
AB
 
106184
AC
 
85653
Other values (578)
1865229 
ValueCountFrequency (%) 
A1827037.2%
 
B1747536.9%
 
C1211954.8%
 
AB1061844.2%
 
AC856533.4%
 
D830173.3%
 
AD740022.9%
 
AE669902.6%
 
AH616272.4%
 
AI587182.3%
 
Other values (573)152087560.0%
 
2020-10-06T09:35:37.650071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-06T09:35:37.918732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length1.691832253
Min length1

No plan
Real number (ℝ≥0)

Distinct6356
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.7908471
Minimum1
Maximum9762
Zeros0
Zeros (%)0.0%
Memory size19.3 MiB
2020-10-06T09:35:38.126796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q187
median223
Q3504
95-th percentile1374
Maximum9762
Range9761
Interquartile range (IQR)417

Descriptive statistics

Standard deviation554.6664385
Coefficient of variation (CV)1.373647874
Kurtosis27.45317251
Mean403.7908471
Median Absolute Deviation (MAD)166
Skewness4.053965317
Sum1023929196
Variance307654.858
MonotocityNot monotonic
2020-10-06T09:35:38.322782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1105970.4%
 
594580.4%
 
394030.4%
 
793090.4%
 
1990910.4%
 
2289570.4%
 
288890.4%
 
688500.3%
 
488110.3%
 
1586190.3%
 
Other values (6346)244380796.4%
 
ValueCountFrequency (%) 
1105970.4%
 
288890.4%
 
394030.4%
 
488110.3%
 
594580.4%
 
ValueCountFrequency (%) 
97621< 0.1%
 
97591< 0.1%
 
97571< 0.1%
 
97311< 0.1%
 
97301< 0.1%
 

No Volume
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2529165
Missing (%)99.7%
Memory size19.3 MiB

1er lot
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1753172
Missing (%)69.1%
Memory size19.3 MiB

Surface Carrez du 1er lot
Categorical

HIGH CARDINALITY
MISSING

Distinct17117
Distinct (%)7.7%
Missing2314726
Missing (%)91.3%
Memory size19.3 MiB
12,50
 
897
12,00
 
449
15,00
 
270
10,00
 
250
13,00
 
245
Other values (17112)
218954 
ValueCountFrequency (%) 
12,50897< 0.1%
 
12,00449< 0.1%
 
15,00270< 0.1%
 
10,00250< 0.1%
 
13,00245< 0.1%
 
30,00223< 0.1%
 
40,00203< 0.1%
 
50,00192< 0.1%
 
25,00190< 0.1%
 
20,00188< 0.1%
 
Other values (17107)2179588.6%
 
(Missing)231472691.3%
 
2020-10-06T09:35:38.614502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3994 ?
Unique (%)1.8%
2020-10-06T09:35:38.815427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.180695885
Min length3

2eme lot
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2372713
Missing (%)93.6%
Memory size19.3 MiB

Surface Carrez du 2eme lot
Categorical

HIGH CARDINALITY
MISSING

Distinct11544
Distinct (%)21.3%
Missing2481517
Missing (%)97.9%
Memory size19.3 MiB
69,16
 
103
20,43
 
70
55,00
 
46
50,00
 
42
67,00
 
41
Other values (11539)
53972 
ValueCountFrequency (%) 
69,16103< 0.1%
 
20,4370< 0.1%
 
55,0046< 0.1%
 
50,0042< 0.1%
 
67,0041< 0.1%
 
65,0039< 0.1%
 
54,0039< 0.1%
 
28,0038< 0.1%
 
60,0037< 0.1%
 
54,4635< 0.1%
 
Other values (11534)537842.1%
 
(Missing)248151797.9%
 
2020-10-06T09:35:39.116805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2982 ?
Unique (%)5.5%
2020-10-06T09:35:39.296066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.044378657
Min length3

3eme lot
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2509709
Missing (%)99.0%
Memory size19.3 MiB

Surface Carrez du 3eme lot
Categorical

HIGH CARDINALITY
MISSING

Distinct3961
Distinct (%)76.0%
Missing2530577
Missing (%)99.8%
Memory size19.3 MiB
20,61
 
69
195,18
 
21
12,50
 
13
15,00
 
12
180,35
 
10
Other values (3956)
5089 
ValueCountFrequency (%) 
20,6169< 0.1%
 
195,1821< 0.1%
 
12,5013< 0.1%
 
15,0012< 0.1%
 
180,3510< 0.1%
 
20,009< 0.1%
 
78,008< 0.1%
 
12,007< 0.1%
 
150,377< 0.1%
 
55,007< 0.1%
 
Other values (3951)50510.2%
 
(Missing)253057799.8%
 
2020-10-06T09:35:39.670270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3152 ?
Unique (%)60.5%
2020-10-06T09:35:40.050640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.004424655
Min length3

4eme lot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct688
Distinct (%)7.4%
Missing2526555
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean113.2001949
Minimum2
Maximum17014
Zeros0
Zeros (%)0.0%
Memory size19.3 MiB
2020-10-06T09:35:40.225978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q17
median24
Q368
95-th percentile349
Maximum17014
Range17012
Interquartile range (IQR)61

Descriptive statistics

Standard deviation523.0737147
Coefficient of variation (CV)4.620784577
Kurtosis300.8019752
Mean113.2001949
Median Absolute Deviation (MAD)19
Skewness14.89310183
Sum1045517
Variance273606.111
MonotocityNot monotonic
2020-10-06T09:35:40.413741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9575< 0.1%
 
8520< 0.1%
 
7505< 0.1%
 
6482< 0.1%
 
4463< 0.1%
 
5463< 0.1%
 
3230< 0.1%
 
2185< 0.1%
 
13147< 0.1%
 
17109< 0.1%
 
Other values (678)55570.2%
 
(Missing)252655599.6%
 
ValueCountFrequency (%) 
2185< 0.1%
 
3230< 0.1%
 
4463< 0.1%
 
5463< 0.1%
 
6482< 0.1%
 
ValueCountFrequency (%) 
170141< 0.1%
 
112791< 0.1%
 
110451< 0.1%
 
110431< 0.1%
 
110411< 0.1%
 

Surface Carrez du 4eme lot
Categorical

HIGH CARDINALITY
MISSING

Distinct1174
Distinct (%)83.0%
Missing2534376
Missing (%)99.9%
Memory size19.3 MiB
20,99
 
68
115,75
 
21
53,93
 
8
12,50
 
7
20,00
 
6
Other values (1169)
1305 
ValueCountFrequency (%) 
20,9968< 0.1%
 
115,7521< 0.1%
 
53,938< 0.1%
 
12,507< 0.1%
 
20,006< 0.1%
 
49,004< 0.1%
 
72,004< 0.1%
 
88,604< 0.1%
 
21,104< 0.1%
 
15,904< 0.1%
 
Other values (1164)12850.1%
 
(Missing)253437699.9%
 
2020-10-06T09:35:40.874764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1064 ?
Unique (%)75.2%
2020-10-06T09:35:41.213158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.001234329
Min length3

5eme lot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct480
Distinct (%)10.8%
Missing2531364
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean113.9613734
Minimum2
Maximum11046
Zeros0
Zeros (%)0.0%
Memory size19.3 MiB
2020-10-06T09:35:41.470831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median27
Q373
95-th percentile359
Maximum11046
Range11044
Interquartile range (IQR)65

Descriptive statistics

Standard deviation452.971562
Coefficient of variation (CV)3.974781529
Kurtosis170.5934583
Mean113.9613734
Median Absolute Deviation (MAD)21
Skewness11.41445096
Sum504507
Variance205183.236
MonotocityNot monotonic
2020-10-06T09:35:42.170166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9271< 0.1%
 
8261< 0.1%
 
5238< 0.1%
 
6228< 0.1%
 
7221< 0.1%
 
4132< 0.1%
 
3114< 0.1%
 
1488< 0.1%
 
6376< 0.1%
 
9376< 0.1%
 
Other values (470)27220.1%
 
(Missing)253136499.8%
 
ValueCountFrequency (%) 
255< 0.1%
 
3114< 0.1%
 
4132< 0.1%
 
5238< 0.1%
 
6228< 0.1%
 
ValueCountFrequency (%) 
110461< 0.1%
 
70362< 0.1%
 
70051< 0.1%
 
61751< 0.1%
 
60741< 0.1%
 

Surface Carrez du 5eme lot
Categorical

HIGH CARDINALITY
MISSING

Distinct473
Distinct (%)77.7%
Missing2535182
Missing (%)> 99.9%
Memory size19.3 MiB
23,79
68 
116,10
 
21
34,84
 
7
12,50
 
5
11,02
 
4
Other values (468)
504 
ValueCountFrequency (%) 
23,7968< 0.1%
 
116,1021< 0.1%
 
34,847< 0.1%
 
12,505< 0.1%
 
11,024< 0.1%
 
21,104< 0.1%
 
41,674< 0.1%
 
19,003< 0.1%
 
55,103< 0.1%
 
21,443< 0.1%
 
Other values (463)487< 0.1%
 
(Missing)2535182> 99.9%
 
2020-10-06T09:35:42.613650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique441 ?
Unique (%)72.4%
2020-10-06T09:35:42.874426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.000544998
Min length3

Nombre de lots
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3943329714
Minimum0
Maximum175
Zeros1753172
Zeros (%)69.1%
Memory size19.3 MiB
2020-10-06T09:35:43.050857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum175
Range175
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8086630828
Coefficient of variation (CV)2.050711306
Kurtosis3551.225082
Mean0.3943329714
Median Absolute Deviation (MAD)0
Skewness28.12730824
Sum999946
Variance0.6539359815
MonotocityNot monotonic
2020-10-06T09:35:43.254936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0175317269.1%
 
161954124.4%
 
21369965.4%
 
3168460.7%
 
448090.2%
 
518310.1%
 
6887< 0.1%
 
7484< 0.1%
 
8296< 0.1%
 
9197< 0.1%
 
Other values (65)732< 0.1%
 
ValueCountFrequency (%) 
0175317269.1%
 
161954124.4%
 
21369965.4%
 
3168460.7%
 
448090.2%
 
ValueCountFrequency (%) 
1751< 0.1%
 
1591< 0.1%
 
1421< 0.1%
 
1411< 0.1%
 
1141< 0.1%
 

Code type local
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1182459
Missing (%)46.6%
Memory size19.3 MiB
1
515398 
2
430975 
3
312480 
4
94479 
ValueCountFrequency (%) 
151539820.3%
 
243097517.0%
 
331248012.3%
 
4944793.7%
 
(Missing)118245946.6%
 
2020-10-06T09:35:43.423164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-06T09:35:43.543715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:35:43.651093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Type local
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1182459
Missing (%)46.6%
Memory size19.3 MiB
Maison
515398 
Appartement
430975 
Dépendance
312480 
Local industriel. commercial ou assimilé
94479 
ValueCountFrequency (%) 
Maison51539820.3%
 
Appartement43097517.0%
 
Dépendance31248012.3%
 
Local industriel. commercial ou assimilé944793.7%
 
(Missing)118245946.6%
 
2020-10-06T09:35:43.806993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-06T09:35:43.911403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:35:44.060071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length6
Mean length7.21055087
Min length3

Identifiant local
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2535791
Missing (%)100.0%
Memory size19.3 MiB

Surface reelle bati
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct3879
Distinct (%)0.3%
Missing1184176
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean87.75654014
Minimum0
Maximum312962
Zeros316200
Zeros (%)12.5%
Memory size19.3 MiB
2020-10-06T09:35:44.260696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median60
Q394
95-th percentile170
Maximum312962
Range312962
Interquartile range (IQR)79

Descriptive statistics

Standard deviation872.8312276
Coefficient of variation (CV)9.946053323
Kurtosis49915.91499
Mean87.75654014
Median Absolute Deviation (MAD)38
Skewness188.7084892
Sum118613056
Variance761834.352
MonotocityNot monotonic
2020-10-06T09:35:44.510341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
031620012.5%
 
80201590.8%
 
60187340.7%
 
90182090.7%
 
70182070.7%
 
50155900.6%
 
100155820.6%
 
65144760.6%
 
40136150.5%
 
75126400.5%
 
Other values (3869)88820335.0%
 
(Missing)118417646.7%
 
ValueCountFrequency (%) 
031620012.5%
 
1271< 0.1%
 
2224< 0.1%
 
3277< 0.1%
 
4155< 0.1%
 
ValueCountFrequency (%) 
3129622< 0.1%
 
2400002< 0.1%
 
2150002< 0.1%
 
2121202< 0.1%
 
1528566< 0.1%
 

Nombre pieces principales
Real number (ℝ≥0)

MISSING
ZEROS

Distinct40
Distinct (%)< 0.1%
Missing1184176
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean2.430351099
Minimum0
Maximum67
Zeros406960
Zeros (%)16.0%
Memory size19.3 MiB
2020-10-06T09:35:44.730489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q34
95-th percentile6
Maximum67
Range67
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.083948134
Coefficient of variation (CV)0.8574679334
Kurtosis2.659994281
Mean2.430351099
Median Absolute Deviation (MAD)2
Skewness0.574144641
Sum3284899
Variance4.342839825
MonotocityNot monotonic
2020-10-06T09:35:44.919147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
040696016.0%
 
42298629.1%
 
32250338.9%
 
21640436.5%
 
51375825.4%
 
11021284.0%
 
6538442.1%
 
7197180.8%
 
872680.3%
 
927160.1%
 
Other values (30)24610.1%
 
(Missing)118417646.7%
 
ValueCountFrequency (%) 
040696016.0%
 
11021284.0%
 
21640436.5%
 
32250338.9%
 
42298629.1%
 
ValueCountFrequency (%) 
671< 0.1%
 
561< 0.1%
 
541< 0.1%
 
532< 0.1%
 
502< 0.1%
 

Nature culture
Categorical

MISSING

Distinct27
Distinct (%)< 0.1%
Missing792958
Missing (%)31.3%
Memory size19.3 MiB
S
839215 
T
258358 
P
130359 
J
93845 
AB
89648 
Other values (22)
331408 
ValueCountFrequency (%) 
S83921533.1%
 
T25835810.2%
 
P1303595.1%
 
J938453.7%
 
AB896483.5%
 
BT747192.9%
 
L639832.5%
 
AG628072.5%
 
VI295071.2%
 
BR249291.0%
 
Other values (17)754633.0%
 
(Missing)79295831.3%
 
2020-10-06T09:35:45.139950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-06T09:35:45.315892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.7599676
Min length1

Nature culture speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct118
Distinct (%)0.1%
Missing2422938
Missing (%)95.5%
Memory size19.3 MiB
POTAG
27853 
PIN
9982 
PARC
9910 
PATUR
9839 
FRICH
6332 
Other values (113)
48937 
ValueCountFrequency (%) 
POTAG278531.1%
 
PIN99820.4%
 
PARC99100.4%
 
PATUR98390.4%
 
FRICH63320.2%
 
VAOC51770.2%
 
CHAT36720.1%
 
IMM35810.1%
 
CHENE26510.1%
 
MARAI26090.1%
 
Other values (108)312471.2%
 
(Missing)242293895.5%
 
2020-10-06T09:35:45.531803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2020-10-06T09:35:45.708099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.065863867
Min length3

Surface terrain
Real number (ℝ≥0)

MISSING
SKEWED

Distinct41332
Distinct (%)2.4%
Missing792958
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean3092.532778
Minimum0
Maximum1662560
Zeros59
Zeros (%)< 0.1%
Memory size19.3 MiB
2020-10-06T09:35:45.968493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1233
median610
Q31885
95-th percentile12680
Maximum1662560
Range1662560
Interquartile range (IQR)1652

Descriptive statistics

Standard deviation13643.41327
Coefficient of variation (CV)4.411727943
Kurtosis3758.552168
Mean3092.532778
Median Absolute Deviation (MAD)486
Skewness44.79390898
Sum5389768179
Variance186142725.7
MonotocityNot monotonic
2020-10-06T09:35:46.208046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
500337441.3%
 
1000153480.6%
 
60050370.2%
 
80049220.2%
 
1243290.2%
 
40042120.2%
 
70041060.2%
 
20039420.2%
 
1339360.2%
 
30039150.2%
 
Other values (41322)165934265.4%
 
(Missing)79295831.3%
 
ValueCountFrequency (%) 
059< 0.1%
 
136540.1%
 
230100.1%
 
326630.1%
 
427670.1%
 
ValueCountFrequency (%) 
16625601< 0.1%
 
14203881< 0.1%
 
14115247< 0.1%
 
133500319< 0.1%
 
132217733< 0.1%
 

Interactions

2020-10-06T09:31:36.593754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:37.443104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:37.651525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:37.780385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:37.912136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:38.304491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:38.424268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:38.536128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:38.663902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:38.772388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:38.900586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:39.033062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:39.274823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:39.412302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:39.535008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:39.695389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:39.824359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:39.944119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:40.083874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:40.204803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:40.320755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:40.436635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:40.609216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:40.753792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:40.890277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:41.003020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:41.151908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:41.357296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:41.527870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:41.709082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:41.825783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:41.953737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:42.069931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:42.226939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:42.370809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:42.475001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:42.723603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:42.848038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:42.963957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:43.076388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:43.220542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:43.353130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:43.504235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:43.620733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:43.785203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:43.993671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:44.109937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:44.226169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:44.331084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:44.454070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:44.575260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:44.747379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:44.863461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:44.983736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:45.111674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:45.263747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:45.376329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:45.504947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:45.625231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:45.749418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:45.893573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:46.078451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:46.210736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:46.319219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:46.459469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:46.579501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:46.683914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:46.832454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:47.065647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:47.233957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:47.350172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:47.518949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:47.639305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:47.758609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:48.035623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:48.332068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:48.532012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:48.660229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:48.780232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:48.896248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.008867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.120824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.232289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.352437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.472126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.592285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.707859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.820422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:49.940435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:50.056210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:50.168864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:50.284661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:50.400750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:50.519593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:50.644452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:50.796642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:50.932576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:51.069266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:51.168344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:51.285068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:51.417190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:51.556915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:51.684644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:51.864439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:52.048249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:52.185145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:52.332548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:52.465087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:52.584584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:52.708787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:52.845072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:52.960963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:53.069262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:53.201034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:53.304644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:53.433616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:53.565146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:53.681008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:53.810420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:53.922486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:54.058558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:54.434709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:54.558181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:54.670821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:54.838715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:55.120446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:55.372183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:55.644596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:55.764420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:56.005120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:56.193172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:56.309400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:56.425971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:56.574127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:56.702137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:56.818520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:56.934324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:57.054542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:57.174986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:57.290675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:57.415124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:57.599061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:57.715356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:31:57.871708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-06T09:35:46.437049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-06T09:35:46.918016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-06T09:35:47.330610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-06T09:35:47.926300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-06T09:35:48.316689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-06T09:32:10.590740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:32:57.075883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:35:02.164348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T09:35:10.880782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Code service CHReference document1 Articles CGI2 Articles CGI3 Articles CGI4 Articles CGI5 Articles CGINo dispositionDate mutationNature mutationValeur fonciereNo voieB/T/QType de voieCode voieVoieCode postalCommuneCode departementCode communePrefixe de sectionSectionNo planNo Volume1er lotSurface Carrez du 1er lot2eme lotSurface Carrez du 2eme lot3eme lotSurface Carrez du 3eme lot4eme lotSurface Carrez du 4eme lot5eme lotSurface Carrez du 5eme lotNombre de lotsCode type localType localIdentifiant localSurface reelle batiNombre pieces principalesNature cultureNature culture specialeSurface terrain
0NaNNaNNaNNaNNaNNaNNaN104/01/2019Vente37220,0026.0NaNRUE2730DE MONTHOLON1000.0BOURG-EN-BRESSE153NaNAI298NaN819,27NaNNaNNaNNaNNaNNaNNaNNaN12.0AppartementNaN20.01.0NaNNaNNaN
1NaNNaNNaNNaNNaNNaNNaN104/01/2019Vente185100,0022.0NaNRUE1650GEN DELESTRAINT1000.0BOURG-EN-BRESSE153NaNAM95NaN137NaN15461,51NaNNaNNaNNaNNaNNaN22.0AppartementNaN62.03.0NaNNaNNaN
2NaNNaNNaNNaNNaNNaNNaN104/01/2019Vente185100,0022.0BRUE1650GEN DELESTRAINT1000.0BOURG-EN-BRESSE153NaNAM95NaN7NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0DépendanceNaN0.00.0NaNNaNNaN
3NaNNaNNaNNaNNaNNaNNaN108/01/2019Vente209000,003.0NaNRUE0043DES CHAMPAGNES1160.0PRIAY1314NaNE1676NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0MaisonNaN90.04.0SNaN940.0
4NaNNaNNaNNaNNaNNaNNaN107/01/2019Vente134900,005.0NaNLOTA003LE BIOLAY1370.0SAINT-ETIENNE-DU-BOIS1350NaNAA11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0MaisonNaN101.05.0SNaN490.0
5NaNNaNNaNNaNNaNNaNNaN103/01/2019Vente192000,00165.0NaNALL0445DES LIBELLULES1340.0ATTIGNAT124NaNAI94NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0MaisonNaN88.04.0SNaN708.0
6NaNNaNNaNNaNNaNNaNNaN108/01/2019Vente45000,009.0NaNRTE0001DU VIADUC1250.0CIZE1106NaNA86NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0MaisonNaN39.02.0SNaN631.0
7NaNNaNNaNNaNNaNNaNNaN108/01/2019Vente45000,00NaNNaNNaNB017SUR LA LATIE1250.0CIZE1106NaNA975NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNLNaN120.0
8NaNNaNNaNNaNNaNNaNNaN104/01/2019Vente65000,0050.0NaNRUE1240DOC NODET1000.0BOURG-EN-BRESSE153NaNAL3NaN317NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0DépendanceNaN0.00.0NaNNaNNaN
9NaNNaNNaNNaNNaNNaNNaN104/01/2019Vente65000,0050.0NaNRUE1240DOC NODET1000.0BOURG-EN-BRESSE153NaNAL3NaN17NaN3367,78NaNNaNNaNNaNNaNNaN22.0AppartementNaN69.03.0NaNNaNNaN

Last rows

Code service CHReference document1 Articles CGI2 Articles CGI3 Articles CGI4 Articles CGI5 Articles CGINo dispositionDate mutationNature mutationValeur fonciereNo voieB/T/QType de voieCode voieVoieCode postalCommuneCode departementCode communePrefixe de sectionSectionNo planNo Volume1er lotSurface Carrez du 1er lot2eme lotSurface Carrez du 2eme lot3eme lotSurface Carrez du 3eme lot4eme lotSurface Carrez du 4eme lot5eme lotSurface Carrez du 5eme lotNombre de lotsCode type localType localIdentifiant localSurface reelle batiNombre pieces principalesNature cultureNature culture specialeSurface terrain
2535781NaNNaNNaNNaNNaNNaNNaN105/12/2019Vente17521000,0032.0NaNQUAI0940DE BETHUNE75004.0PARIS 0475104NaNAU11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN03.0DépendanceNaN0.00.0SNaN470.0
2535782NaNNaNNaNNaNNaNNaNNaN105/12/2019Vente17521000,0032.0NaNQUAI0940DE BETHUNE75004.0PARIS 0475104NaNAU11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0AppartementNaN70.03.0SNaN470.0
2535783NaNNaNNaNNaNNaNNaNNaN105/12/2019Vente17521000,0032.0NaNQUAI0940DE BETHUNE75004.0PARIS 0475104NaNAU11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0AppartementNaN47.01.0SNaN470.0
2535784NaNNaNNaNNaNNaNNaNNaN105/12/2019Vente17521000,0032.0NaNQUAI0940DE BETHUNE75004.0PARIS 0475104NaNAU11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0AppartementNaN55.02.0SNaN470.0
2535785NaNNaNNaNNaNNaNNaNNaN105/12/2019Vente17521000,0032.0NaNQUAI0940DE BETHUNE75004.0PARIS 0475104NaNAU11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0AppartementNaN66.04.0SNaN470.0
2535786NaNNaNNaNNaNNaNNaNNaN105/12/2019Vente17521000,0032.0NaNQUAI0940DE BETHUNE75004.0PARIS 0475104NaNAU11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0AppartementNaN120.05.0SNaN470.0
2535787NaNNaNNaNNaNNaNNaNNaN105/12/2019Vente17521000,0032.0NaNQUAI0940DE BETHUNE75004.0PARIS 0475104NaNAU11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN03.0DépendanceNaN0.00.0SNaN470.0
2535788NaNNaNNaNNaNNaNNaNNaN110/10/2019Adjudication610000,0012.0NaNRUE0797BEAUTREILLIS75004.0PARIS 0475104NaNAQ40NaN2NaN35NaNNaNNaNNaNNaNNaNNaN22.0AppartementNaN44.02.0NaNNaNNaN
2535789NaNNaNNaNNaNNaNNaNNaN130/12/2019Vente1400000,0024.0NaNRUE8752SAINT SAUVEUR75002.0PARIS 0275102NaNAM18NaN3101,40443,708NaNNaNNaNNaNNaN34.0Local industriel. commercial ou assimiléNaN100.00.0NaNNaNNaN
2535790NaNNaNNaNNaNNaNNaNNaN130/12/2019Vente1400000,0024.0NaNRUE8752SAINT SAUVEUR75002.0PARIS 0275102NaNAM18NaN1043,701455,40NaNNaNNaNNaNNaNNaN22.0AppartementNaN97.03.0NaNNaNNaN